在癌症中快速预测热力学不稳定的铁酸酸化
Jaie Woodard1, Zhengqing Liu1, Atena Malemir Chegini2
1Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA.
bioRxiv : the preprint server for biology
|October 1, 2025
概括
我们开发了一种机器学习方法来预测氨酸酸化如何影响蛋白质稳定性,识别癌症中不稳定的酸化. 这有助于选潜在的致癌突变.
科学领域:
- 生物化学 生化学
- 计算生物学 计算生物学
- 在瘤学瘤学.
背景情况:
- 铁酸酸化是癌症发展的关键.
- 通过计算分析蛋白质组对于识别功能失调的酸化是至关重要的.
研究的目的:
- 开发一种机器学习 (ML) 模型,预测由氨酸酸化引起的热力学稳定性的变化.
- 将该模型应用于泛癌数据集,以确定各种癌症类型中的破坏稳定的酸化.
主要方法:
- 使用结构特征预测相仿的delta-delta-G.
- 与实验性突变扫描cDNA蛋白质分解数据的相关性 (R = 0.71).
- 应用到Alphafold2数据库和瘤蛋白组学数据集.
- 使用极端梯度增强的ML来预测自身抑制状态 (AUC = 0.85).
主要成果:
- ML模型准确地预测了热力学稳定性的变化.
- 在瘤基因和瘤抑制剂中预测出不稳定的酸化.
- 与其他蛋白质相比,自抑制性瘤基因显示出明显的电路拓特征.
- 在化学和氧化应激路径中观察到不稳定蛋白质的丰富.
- 预计58%的复发性铁素癌酸化是破坏稳定的.
结论:
- 开发的计算方法可以快速选破坏稳定的酸化和相对应突变.
- 这些发现突出了酸化在蛋白质稳定性和癌症机制中的作用.
- 这项研究为癌症研究和药物发现提供了宝贵的工具.
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